Vehicles may be equipped with sensors that facilitate perceiving other vehicles, obstacles, pedestrians, and additional aspects of a surrounding environment. For example, a vehicle may be equipped with a light detection and ranging (LIDAR) sensor that uses light to scan the surrounding environment, while logic associated with the LIDAR analyzes acquired data to detect a presence of objects and other features of the surrounding environment. In further examples, additional/alternative sensors such as cameras may be implemented to acquire information about the surrounding environment from which a system derives awareness about aspects of the surrounding environment. This sensor data can be useful in various circumstances for improving perceptions of the surrounding environment so that systems such as autonomous driving systems can perceive the noted aspects and accurately plan and navigate accordingly.
In general, the further awareness is developed by the vehicle about a surrounding environment, the better a driver can be supplemented with information to assist in driving and/or the better an autonomous system can control the vehicle to avoid hazards. However, the sensor data acquired by the various sensors generally includes some amount of error. Thus, intrinsically trusting individual identifications of vehicles using a single observation, for example, can result in false detections and misleading information provided to the driver and/or autonomous systems.
In the context of lane identification, observations of a surrounding environment by sensors of the vehicle may be used to identify lanes for lane keeping functions, and other autonomous operations. However, as lane markings vary in quality and type, identifying lanes and correlating lanes with observed vehicles represents a unique difficulty. That is, determining whether a surrounding vehicle is traveling within a particular lane can compound difficulties associated with interpreting sensor data for ensuring accurate detection of a vehicle, determining a precise location of the vehicle, and detection boundaries of lanes, and so on. Thus, identifying which lanes are occupied by surrounding vehicles can represent a significant difficulty. As such, determining the occupancy of lanes surrounding a vehicle in support of autonomous maneuvers such as path planning, lane changing, and so on while useful for the noted purposes represents a task that is compounded by several difficulties.